Abstract
Person re-identification plays a key role in applications where a mobile robot needs to track its users over a long period of time, even if they are partially unobserved for some time, in order to follow them or be available on demand. In this context, deep-learning based real-time feature extraction on a mobile robot is often performed on special-purpose devices whose computational resources are shared for multiple tasks. Therefore, the inference speed has to be taken into account. In contrast, person re-identification is often improved by architectural changes that come at the cost of significantly slowing down inference. Attention blocks are one such example. We will show that some well-performing attention blocks used in the state of the art are subject to inference costs that are far too high to justify their use for mobile robotic applications. As a consequence, we propose an attention block that only slightly affects the inference speed while keeping up with much deeper networks or more complex attention blocks in terms of re-identification accuracy. We perform extensive neural architecture search to derive rules at which locations this attention block should be integrated into the architecture in order to achieve the best trade-off between speed and accuracy. Finally, we confirm that the best performing configuration on a re-identification benchmark also performs well on an indoor robotic dataset.
Abstract (translated)
人重新配对在需要对移动机器人的用户进行长期跟踪的应用中发挥着关键作用,即使他们部分被观察了一段时间,以便跟随他们或随时可用。在这种情况下,基于深度学习的实时特征提取通常在特殊的专用设备上进行,这些设备的计算资源被共享用于多个任务。因此,推断速度必须考虑到。相比之下,人重新配对通常通过建筑结构改变来实现,这样做的代价是显著减缓推断速度。注意力块就是一个这样的例子。我们将证明,一些先进的注意力块在常用的设计中表现良好,但推断成本却非常高,以至于不能将其用于移动机器人应用。因此,我们提出了一个注意力块,它只略微影响推断速度,而能够在人重新配对精度方面与更深层的网络或更复杂的注意力块保持同步。我们进行了广泛的神经网络架构搜索,以推导出该注意力块应该嵌入到架构中的特定位置的规则,以实现速度与精度的最佳权衡。最后,我们确认,在人重新配对基准测试中表现最佳的配置也在室内机器人数据集上表现良好。
URL
https://arxiv.org/abs/2302.14574
